BLUE BELL, Pa., March 15, 2017 /PRNewswire/ -- Unisys Corporation
(NYSE: UIS) announced today the launch of its
Machine-Learning-as-a-Service, a new service offering that enables
organizations to predict changes in business conditions and quickly
evaluate potential responses to those changes.
Available as part of the Unisys Analytics Platform, the new
service combines a library of machine learning algorithms with a
suite of proven methodologies and processes that analyze and
extract important details and insights from clients' data to
generate prescriptive reporting – analytics that forecast future
business and operating conditions for clients, but also recommend
future actions to facilitate and support decision-making.
"Unisys Machine-Learning-as-a-Service not only facilitates
predictive analytics, it helps organizations transform and optimize
their business processes. By providing an unmatched combination of
skilled data scientists and vertical domain expertise, Unisys can
offer unique insight into our clients' business and how analytics
can best transform them to new levels of success," said Dr.
Rod Fontecilla, vice president and
global lead for analytics at Unisys. "With our new
Machine-Learning-as-a-Service offering, we can give our clients a
scalable platform running in the cloud or on premise to drive
business insights and mission effectiveness."
On the Cutting Edge
Machine learning is a type of
artificial intelligence that enables computers to constantly
optimize and improve without human intervention. Using algorithms
that iteratively draw lessons from data, machine learning systems
identify patterns and find hidden insights without being explicitly
programmed. This ability to apply complex calculations dynamically
and repeatedly on large data sets allows organizations to assess
current conditions, as well as anticipate future circumstances and
strategically position themselves accordingly.
Unisys Machine-Learning-as-a-Service leverages agile development
principles to help clients understand and work with their data.
Engagements begin with a proof-of-concept phase in which Unisys
data scientists partner with the client's subject matter experts to
identify available data and get an overall understanding of their
business needs and objectives. From this close collaboration,
Unisys implements machine learning algorithms to analyze the
client's data and then develop advanced data analytics models for
predicting outcomes.
Following the successful proof-of-concept, Unisys and the client
move to the production phase and continue the agile engagement
process to expand and formalize the analytical framework, while
leveraging additional data and predictive models and continuing to
monitor and refine the accuracy and integrity of the models and
their results.
"This dynamic and iterative process helps clients use early
insights to drive new questions and make new predictions from their
data with very little investment on their part," Fontecilla said.
"This provides a quick and reliable way for clients to experiment
before making significant investments."
Fontecilla spoke on the topic of machine-learning opportunities
in the airline industry at this week's Strata + Hadoop World
conference in San Jose, Calif. For
more information on Fontecilla's presentation, please click here:
https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/55694.
About Unisys
Unisys is a global information technology
company that specializes in providing industry-focused solutions
integrated with leading-edge security to clients in the government,
financial services and commercial markets. Unisys offerings include
security solutions, advanced data analytics, cloud and
infrastructure services, application services and application and
server software. For more information, visit www.unisys.com.
Follow Unisys on Twitter and LinkedIn.
RELEASE NO.: 0315/9487
Unisys and other Unisys products and services mentioned herein,
as well as their respective logos, are trademarks or registered
trademarks of Unisys Corporation. Any other brand or product
referenced herein is acknowledged to be a trademark or registered
trademark of its respective holder.
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SOURCE Unisys Corporation